if (!requireNamespace("tidyverse", quietly = TRUE))
install.packages('tidyverse')
if (!requireNamespace("Seurat", quietly = TRUE))
install.packages('Seurat')
if (!requireNamespace("compositions", quietly = TRUE))
install.packages('compositions')
if (!requireNamespace("colorBlindness", quietly = TRUE))
install.packages('colorBlindness')
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!requireNamespace("EnhancedVolcano", quietly = TRUE))
BiocManager::install("EnhancedVolcano")
if (!requireNamespace("ComplexHeatmap", quietly = TRUE))
BiocManager::install("ComplexHeatmap")
if (!requireNamespace("scales", quietly = TRUE))
install.packages('scales')
if (!requireNamespace("viridis", quietly = TRUE))
install.packages('viridis')
if (!requireNamespace("DT", quietly = TRUE))
install.packages('DT')
if (!requireNamespace("reshape2", quietly = TRUE))
install.packages('reshape2')
if (!requireNamespace("ggrepel", quietly = TRUE))
install.packages("ggrepel")
if (!requireNamespace("sccomp", quietly = TRUE))
devtools::install_github("stemangiola/sccomp")4 - Compositional Analysis
Introduction
In this notebook we will study which cell types compose different samples in single-cell data. We will do this using a subset of statistics called compositional data analysis (or CoDA for short), because single-cell experiments are small samples of a larger tissue so the cells we obtain represent only the proportions of each cell type in the tissue, not their true abundance.
CoDA relativizes proportions by comparing them to a stable population, comparing them to each other, or to a mean across samples, all resulting in a table of relative abundances of cell types per sample. This sample x species matrix is a natural foundation for conducting case-control analysis, as we have a single vector of variables per sample. Here, we can scrutinize variances between conditions and between samples.
With compositions, we can be as granular as our data allows us. Some studies [Zheng et al. (2023)](Dann et al. 2022) have suggested that higher-resolution sub-clusters are where biological differences are most prominent. Compositions can be normalized within cell type or subtype so case-control analysis can be performed at this level, with multiple resolutions using advanced methods, or at a more general low-resolution clustering level. I’ve written a tool for automated sub-clustering and sub-cluster annotation, ARBOL, which is available in R and python.
For teaching purposes, here we will use the cell type annotations the authors provide and include all celltypes in our compositional analysis.
Useful Resources
- John Aitchison’s Compositional Data Analysis (J. Aitchison 1982)
- scCODA, a python + scanpy package for ALR compositional analysis (Büttner et al. 2021)
- Cacoa, an R package for case-control analysis that uses ILR
- compositions, an R package for CoDA
- A paper describing the compositionality problem in terms of microbiome studies (Morton et al. 2019)
- sccomp - an R method for single-cell compositional comparisons https://github.com/stemangiola/sccomp
Key Takeaways
- Compositions provide a good foundation for comparing individual samples
- The compositional nature of scRNAseq means only conclusions about relative values can be made, and overall cell density can bias results
- Compositional transforms allow quantitative assessment of scRNA relative abundances
Libraries
Installation
Load Libraries
library(colorBlindness)
library(tidyverse)
library(EnhancedVolcano)
library(viridis)
library(scales)
library(DT)
library(Seurat)
library(compositions)
library(reshape2)
library(ComplexHeatmap)
library(ggrepel)
library(sccomp)Load data
srobj <- readRDS('../data/d8e35450-de43-451a-9979-276eac688bce.rds')
genes <- read_csv('../data/cov_flu_gene_names_table.csv') # Load a provided gene conversion table to convert ENSG to readable gene symbols
# Need to remake seurat object
mtx <- srobj@assays$RNA@data
rownames(mtx) <- genes[match(row.names(mtx),genes$index),]$feature_name
srobj <- CreateSeuratObject(counts = mtx, meta.data = srobj@meta.data)
srobjAn object of class Seurat
33234 features across 59572 samples within 1 assay
Active assay: RNA (33234 features, 0 variable features)
1 layer present: countsset.seed(687)Other setup
Generate a color palette for plotting
# https://www.datanovia.com/en/blog/easy-way-to-expand-color-palettes-in-r/
# nb.cols <- length(unique(se$Celltype))
# mycolors <- colorRampPalette(paletteMartin)(nb.cols)
pal <- paletteMartin
names(pal) <- sort(unique(srobj$Celltype))Generate a sample x celltype composition table
In papers we often see percentages of clusters per sample or of subclusters in a larger celltype. These are done using stacked bar plots as follows.
celltypePercentagesDF <- srobj@meta.data %>%
count(`Sample ID`,Celltype) %>%
group_by(`Sample ID`) %>%
reframe(Celltype, celltype_n = n, total_n_cells = sum(n)) %>%
mutate(pct_celltype = celltype_n / total_n_cells)
datatable(celltypePercentagesDF)celltypePercentagesDF %>%
ggplot(aes(x = `Sample ID`, y = pct_celltype, fill = Celltype),
color = 'white') +
geom_bar(position='stack', stat='identity') +
scale_fill_manual(values = pal) +
ggtitle('Composition of each sample by celltype') +
theme_linedraw(base_size = 20) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
Understanding Compositional Data Analysis
Comparing compositions with Wilcoxon tests
For simplicity all comparisons in this notebook will be made between Flu and normal samples, with Flu in the positive direction.
Compositions are often compared using rank sum tests, which Morton et al (Morton et al. 2019) show is a great way to avoid the compositionality problem. It avoids the problem by only making conclusions about relative abundance rather than absolute abundance, because a cell type’s rank is dependent on the other cell types. The most commonly used rank test is the Wilcoxon test (aka Mann-Whitney U test), the default test Seurat uses for comparisons of genes across clusters with FindAllMarkers.
If we use the Wilcoxon test here in a comparison of compositions across samples Flu vs. Normal, we will see that the result depends on proper normalization of the data.
# Create a function to perform Wilcoxon rank-sum tests and return p-values
perform_wilcox_test <- function(data, group_col, group1, group2, species_col) {
group1_data <- data[data[[group_col]] == group1, species_col]
group2_data <- data[data[[group_col]] == group2, species_col]
test_result <- wilcox.test(group1_data, group2_data)
return(data.frame(
p.val = test_result$p.value,
statistic = test_result$statistic,
species = species_col,
test = paste(group1,'vs',group2)))
}
rawTb <- celltypePercentagesDF %>%
dplyr::select(sample=`Sample ID`, Celltype, celltype_n) %>%
pivot_wider(names_from=Celltype, values_from= celltype_n, values_fill=0) %>%
separate(sample, sep = " ", into=c('group','sample_n'), remove=FALSE) %>%
column_to_rownames("sample") %>%
select(-sample_n)
# Create a long dataframe of raw values for comparisons
raw_long <- rawTb %>%
pivot_longer(-group, names_to="species", values_to="counts") %>%
mutate(transform = "Raw Counts")
species_col_names <- colnames(rawTb)[2:ncol(rawTb)]
wilcoxon_raw <- lapply(species_col_names, function(species)
perform_wilcox_test(rawTb,
group_col = "group",
group1 = 'Flu',
group2 = 'Normal',
species_col = species))
wilcoxon_raw <- wilcoxon_raw %>%
bind_rows %>%
data.frame %>%
mutate(transform = "Raw")
pctTb <- celltypePercentagesDF %>%
dplyr::select(sample=`Sample ID`, Celltype, pct_celltype) %>%
pivot_wider(names_from=Celltype, values_from=pct_celltype, values_fill=0) %>%
separate(sample, sep = " ", into=c('group','sample_n'), remove=FALSE) %>%
column_to_rownames("sample") %>%
select(-sample_n) # reorganize dataframe for wilcoxon tests
# Create a long dataframe of percent values for comparisons
pct_long <- pctTb %>%
pivot_longer(-group, names_to="species", values_to="counts") %>%
mutate(transform = "Percentages")
wilcoxon_pct <- lapply(species_col_names, function(species)
perform_wilcox_test(pctTb,
group_col = "group",
group1 = 'Flu',
group2 = 'Normal',
species_col = species))
wilcoxon_pct <- wilcoxon_pct %>% bind_rows %>% data.frame %>% mutate(transform = "Percent")
combined_data <- bind_rows(raw_long, pct_long)
combined_data %>%
filter(group %in% c('Flu','Normal')) %>%
ggplot(aes(x = species, y=counts, fill = group)) +
geom_boxplot() +
facet_wrap(~ transform, scales = "free_y") +
labs(title = "Comparison of Species Counts per Group",
x = "Species",
y = "Counts / Percentages") +
theme_minimal(base_size = 20) +
theme(axis.text.x = element_text(angle=45, hjust=1),
legend.title = element_blank()) +
scale_fill_manual(values = unname(pal[c(1,4)]))
wilcoxon_comparisons <- bind_rows(wilcoxon_raw,wilcoxon_pct)
ggplot(wilcoxon_comparisons, aes(x = reorder(species, p.val), y = p.val, color = p.val < 0.05)) +
geom_point() + # Use geom_bar() for bar plots
scale_color_manual(values = c("TRUE" = "red", "FALSE" = "blue"), name = "p-val < 0.05") +
facet_wrap(~ transform, scales = "free_x") +
labs(x = "Species", title = "Comparison of P-Values Across Species and Transformations") +
theme_minimal(base_size = 20) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "top") +
guides(fill = guide_legend(title = "P-value < 0.05"))
Compositional transforms
The compositions package provides compositional transforms that allow quantitative comparisons of compositional data. In the most popular package for compositional analysis in scRNA, scCODA, the additive-log-ratio is used, where compositions are transformed to log-ratios of the cluster with the least dispersion that is present across 95% of all samples.
\text{ALR}(x_i) = \log\left(\frac{x_i}{x_D}\right)
where:
- \log denotes the natural logarithm,
- x_i is the value of component i in the composition,
- x_D is the chosen reference component from the compositional dataset.
\text{CLR}(x_i) = \log\left(\frac{x_i}{g(x)}\right)
where:
- \log denotes the natural logarithm,
- x_i is the value of component i in the composition,
- g(x) is the geometric mean of all components in the composition, calculated as g(x) = \left(\prod_{i=1}^{D} x_i\right)^{\frac{1}{D}}, with D being the total number of components in the composition. \prod_{} means multiply each x_i, so you can think of it as an extension of the pythagorean theorem to any n components
The CLR is very easy to use in practice because it does not require a reference. In datasets with very noisy cell subtypes or very different samples, it can be better than the ALR, which can fail when there isn’t a good reference cluster. For this notebook, we can calculate the ALR in a similar way to what scCODA (the first CODA package built into scanpy) does by choosing the reference based on which cluster has minimal dispersion and at least 95% presence across samples
compTb <- pctTb
clrTb <- compositions::clr(compTb[,-1]) %>% data.frame
colnames(clrTb) <- species_col_names
clrTb$group <- compTb$group
# Create a long dataframe of raw values for comparisons
clr_long <- clrTb %>%
pivot_longer(-group, names_to="species", values_to="counts") %>%
mutate(transform = "CLR")
wilcoxon_clr <- lapply(species_col_names, function(species)
perform_wilcox_test(clrTb,
group_col = "group",
group1 = 'Flu',
group2 = 'Normal',
species_col = species))
wilcoxon_clr <- wilcoxon_clr %>% bind_rows %>% data.frame %>% mutate(transform = "CLR")
presence_threshold <- 0.95
presence <- apply(compTb > 0, 2, function(x) mean(x)) > presence_threshold
# make sure group column isn't included
presence[1] <- FALSE
iqr_values <- apply(compTb[, presence], 2, IQR)
denominator_index <- which.min(iqr_values)
denominator_name <- names(iqr_values[denominator_index])
alrTb <- compositions::alr(rawTb[,-1] + 1, # apply a tiny pseudocount to avoid logarithmizing a 0
ivar = denominator_name) %>% #ivar is the invariant variable
data.frame
alr_col_names <- species_col_names[species_col_names!=denominator_name]
colnames(alrTb) <- alr_col_names
alrTb$group <- compTb$group
alr_long <- alrTb %>%
pivot_longer(-group, names_to="species", values_to="counts") %>%
mutate(transform = "ALR")
combined_data <- bind_rows(combined_data, clr_long, alr_long)
ggplot(combined_data %>% filter(group %in% c('Flu','Normal')),
aes(x = species, y=counts, fill = group)) +
geom_boxplot() +
labs(title = "Comparison of Species Counts per Group",
x = "Species",
y = "values") +
theme_minimal(base_size = 20) +
theme(axis.text.x = element_text(angle=45, hjust=1),
legend.title = element_blank()) +
scale_fill_manual(values = unname(pal[c(1,4)])) +
facet_wrap(~ transform, scales = "free_y")
wilcoxon_alr <- lapply(alr_col_names, function(species)
perform_wilcox_test(pctTb,
group_col = "group",
group1 = 'Flu',
group2 = 'Normal',
species_col = species))
wilcoxon_alr <- wilcoxon_alr %>% bind_rows %>% data.frame %>% mutate(transform = "ALR")
wilcoxon_comparisons <- bind_rows(wilcoxon_comparisons,wilcoxon_clr,wilcoxon_alr)ggplot(wilcoxon_comparisons, aes(x = reorder(species, p.val), y = p.val, color = p.val < 0.05)) +
geom_point() +
scale_color_manual(values = c("TRUE" = "red", "FALSE" = "blue"), name = "p-val < 0.05") +
facet_wrap(~ transform, scales = "free_x") +
labs(x = "Species", title = "Comparison of P-Values Across Species and Transformations") +
theme_minimal(base_size = 20) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
legend.position = "top") +
guides(fill = guide_legend(title = "P-value < 0.05"))
We can see that calculating rank sum differences across samples can produce a lot of false positives if we don’t normalize our data properly. But even with mere normalization to overall cell number, the Wilcoxon shows similar results to ALR. This is because the ranks of cell population proportions do not change much with normalization. If we used a Bayesian or parametric test, we would find more false positives with the percentages than with the compositional transforms.
The “best practice” method that has been shown to be successful for compositional analysis in microbiome (“ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq PLOS ONE” n.d.) and single-cell data (Mangiola et al. 2023) is composition-transform paired with binomial distribution modeling ANOVA tests. sccomp is available on github
Estimating a size effect in compositional data
We can see that calculating rank sum differences across samples can produce a lot of false positives if we don’t normalize our data properly. But even with mere normalization to overall cell number, the Wilcoxon shows similar results to ALR. This is because the ranks of cell population proportions do not change much with normalization. If we used a Bayesian or parametric test, we would find more false positives with the percentages than with the compositional transforms.
The “best practice” method that has been shown to be successful for compositional analysis in microbiome (“ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq PLOS ONE” n.d.) and single-cell data (Mangiola et al. 2023) is composition-transform paired with binomial distribution modeling ANOVA tests. sccomp is available on github
srobj$sample <- srobj$`Sample ID` %>% str_replace_all(' ','')
srobj$disease <- srobj$`Sample ID` %>% str_replace_all("\\ .*","")
sccomp_est <- subset(srobj, disease %in% c('Flu','Normal')) %>%
sccomp_estimate(
formula_composition = ~ 0 + disease,
formula_variability = ~ 0 + disease,
.sample = sample,
.cell_group = Celltype,
bimodal_mean_variability_association = TRUE,
cores = 4
)
# Run post-model processing
sccomp_res <- sccomp_est %>%
sccomp_remove_outliers() %>%
sccomp_test(contrasts = "diseaseFlu - diseaseNormal",
test_composition_above_logit_fold_change = 0.2)The plotting methods don’t work well in the sccomp package. I recommend using the data to make plots directly. A familiar plot for differential tests is the volcano plot.
EnhancedVolcano(sccomp_res,
x = "c_effect",
y = "c_FDR",
lab = sccomp_res$Celltype,
pCutoff = 0.05,
title = "sccomp differential composition Flu vs. Normal",
subtitle = "p-val is FDR. FDR cutoff = 0.05; Flu positive, Normal negative")
In sccomp, a differential composition “c” and differential variability “v” are both calculated between groups. We can plot each of these separately as well
sccomp_res <- sccomp_res %>%
mutate(signif = ifelse(c_FDR < 0.05, "FDR < 0.05", "FDR >= 0.05")) %>% arrange(c_effect)
ggplot(sccomp_res, aes(y = factor(Celltype,levels=Celltype), x = c_effect)) +
geom_segment(aes(y = Celltype,
yend = Celltype,
x = c_lower,
xend = c_upper,
color = signif),
size = 1) +
geom_point(size = 3) + # Add point at c_effect
scale_color_manual(values = c("FDR < 0.05" = "red3", "FDR >= 0.05" = "black")) +
theme_minimal(base_size = 16) +
labs(title = "Composition test intervals with FDR",
x = "Log-fold-change",
y = "Celltype") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_blank()) # Improve x-axis labels readability
sccomp_res <- sccomp_res %>%
mutate(signif = ifelse(v_FDR < 0.05, "FDR < 0.05", "FDR >= 0.05")) %>% arrange(v_effect)
ggplot(sccomp_res, aes(y = factor(Celltype,levels=Celltype), x = v_effect)) +
geom_segment(aes(y = Celltype,
yend = Celltype,
x = v_lower,
xend = v_upper,
color = signif),
size = 1) +
geom_point(size = 3) + # Add point at c_effect
scale_color_manual(values = c("FDR < 0.05" = "red3", "FDR >= 0.05" = "black")) +
theme_minimal(base_size = 16) +
labs(title = "Variability test intervals with FDR",
x = "Difference in Variance",
y = "Celltype") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_blank()) # Improve x-axis labels readability
When there are many cell states in a dataset, for example in a large dataset, it can be nice to make a volcano plot of results for interpretability, so here we include code for creating a nice looking one based on sccomp results
Comparing samples with compositions
PCA
comp_ls <- list("alr" = alrTb, "clr" = clrTb, "raw" = rawTb, "pct" = pctTb)
# Run PCA on each transformation
pca_ls <- lapply(names(comp_ls), function(i) {
PCA <- comp_ls[[i]] %>%
dplyr::select(-group) %>%
prcomp()
})
names(pca_ls) <- names(comp_ls)
# Extract data of interest from each transformation
pcaDF <- lapply(names(pca_ls), function(i) {
# Return dataframe
data.frame(
pca_ls[[i]]$x,
group = comp_ls[[i]]$group,
transform = i)
}) %>%
bind_rows() %>%
rownames_to_column("sample_id") %>%
mutate(
sample_id = str_replace_all(sample_id, '\\.\\.\\.(.*)', ''))
# add total number of cells per sample to the PCA for illustration
total_cells <- srobj@meta.data %>% count(`Sample ID`,`Disease group`, Severity, development_stage) %>% rename(total_cells = n, sample_id=`Sample ID`)
pcaDF <- pcaDF %>% left_join(total_cells)
ggplot(pcaDF, aes(x = PC1, y = PC2, color = group, label = sample_id)) +
geom_point(size=4) +
geom_text_repel() +
facet_wrap(~ transform, scales = "free") +
labs(title = "PCA Across Transformations") +
theme_linedraw(base_size = 20) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom") +
guides(fill = guide_legend(title = "P-value < 0.05")) +
scale_color_manual(values = unname(pal[c(4,6,9)]))
ggplot(pcaDF, aes(x = PC1, y = PC2, color = total_cells, label = sample_id)) +
geom_point(size=4) +
geom_text_repel() +
facet_wrap(~ transform, scales = "free") +
labs(title = "PCA colored by total cell number") +
theme_linedraw(base_size = 20) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.text = element_text(angle=45, hjust = 1),
legend.position = "bottom") +
scale_color_gradient(low = 'turquoise', high='royalblue')
ggplot(pcaDF, aes(x = PC1, y = PC2, color = `Disease group`, label = sample_id)) +
geom_point(size=4) +
geom_text_repel() +
facet_wrap(~ transform, scales = "free") +
labs(title = "PCA colored by patient severity") +
theme_linedraw(base_size = 20) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom") +
scale_color_manual(values = unname(pal[-2])) +
guides(color = guide_legend(nrow = 2, byrow = TRUE))
loadingsDF <- lapply(names(pca_ls), function(i) {
data.frame(
pca_ls[[i]]$rotation,
transform = i)
}) %>%
bind_rows() %>%
rownames_to_column('species') %>%
group_by(transform) %>%
arrange(PC1)
loadingsDF %>%
ggplot(
aes(x = PC1, y = factor(species, levels = loadingsDF$species),
fill = PC1 < 0)) +
geom_bar(stat = 'identity') +
facet_wrap(.~transform, scales = 'free') +
theme_linedraw(base_size = 20) +
scale_fill_manual(values = unname(pal[c(6, 11)])) #+
# scale_y_discrete(labels = function(x) str_replace_all(x, '\\.\\.\\.(.*)',''))Distances between samples
We can also directly calculate distances between samples based on sample composition space. This can give us a better idea about outliers and how samples group together. We can also use any of the clustering metrics to test coherence of groups.
In compositional analysis, the method for calculating distances between samples proposed by John Aitchison (John Aitchison 2005) remains the a popular method. Termed Aitchison Distance, it is the Euclidean distance between samples based on CLR transformed counts of species.
clr_distances <- dist(clrTb[,-16], method = "euclidean")
distdf <- reshape2::melt(as.matrix(clr_distances), varnames = c("from", "to"))
datatable(distdf)distlong <- distdf %>% separate(from,c('group1','sample1'),sep=' ', remove=FALSE) %>%
separate(to,c('group2','sample2'),sep=' ', remove=FALSE)
distlong %>% filter(sample1 !=sample2) %>%
mutate(distance_group = ifelse(group1==group2,
'between same disease individuals',
ifelse(group1 == 'Normal' | group2 == 'Normal',
'between disease and healthy',
'between Covid and Flu'))) %>%
ggplot(aes(x=value,fill=distance_group)) + geom_density(alpha=0.4) +
ggtitle('COVID-Flu study distances\nAitchison Distances') +
theme_bw(base_size = 20) + scale_fill_manual(values = unname(pal[c(4,6,3)])) + xlab('euclidean distance')
heatmap_metadata <- pcaDF %>%
dplyr::select(sample_id,`Disease group`) %>%
distinct %>%
mutate(severity = ifelse(grepl("severe", `Disease group`), "Severe", "Mild"),
disease = sapply(strsplit(sample_id, " "), function(x) x[1])
)
color_gradient <- colorRampPalette(c("#eff817", "#fe1b07"))(100)
unnamed_pal <- unname(pal)
ha <- HeatmapAnnotation(df = as.data.frame(heatmap_metadata[, c("severity", "disease")]),
col = list(severity = c("Severe" = unnamed_pal[7], "Mild" = unnamed_pal[10]),
disease = c("Flu" = unnamed_pal[6], "nCoV" = unnamed_pal[4], "Normal" = unnamed_pal[1])),
which = "column")
ComplexHeatmap::Heatmap(as.matrix(clr_distances),
name = "Aitchison Distance",
col = color_gradient,
top_annotation = ha,
clustering_distance_rows = "euclidean",
clustering_distance_columns = "euclidean")
Session Info
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] sccomp_1.7.6 ComplexHeatmap_2.18.0 reshape2_1.4.4 compositions_2.0-8 Seurat_5.0.2 SeuratObject_5.0.1 sp_2.1-3 DT_0.32 scales_1.3.0 viridis_0.6.5 viridisLite_0.4.2 EnhancedVolcano_1.20.0 ggrepel_0.9.5 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0 colorBlindness_0.1.9 BiocManager_1.30.22 knitr_1.45
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.1 later_1.3.2 bitops_1.0-7 polyclip_1.10-6 fastDummies_1.7.3 lifecycle_1.0.4 StanHeaders_2.32.6 doParallel_1.0.17 vroom_1.6.5 globals_0.16.3 lattice_0.22-5 MASS_7.3-60.0.1 crosstalk_1.2.1 magrittr_2.0.3 sass_0.4.8 plotly_4.10.4 rmarkdown_2.26 jquerylib_0.1.4 yaml_2.3.8 httpuv_1.6.14 sctransform_0.4.1 spam_2.10-0 pkgbuild_1.4.3 spatstat.sparse_3.0-3 reticulate_1.35.0 cowplot_1.1.3 pbapply_1.7-2 bayesm_3.1-6 RColorBrewer_1.1-3 abind_1.4-5 zlibbioc_1.48.0 Rtsne_0.17 GenomicRanges_1.54.1 RCurl_1.98-1.14 BiocGenerics_0.48.1 tensorA_0.36.2.1 circlize_0.4.16 GenomeInfoDbData_1.2.11 IRanges_2.36.0 S4Vectors_0.40.2 inline_0.3.19
[43] irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.0-4 goftest_1.2-3 RSpectra_0.16-1 spatstat.random_3.2-3 fitdistrplus_1.1-11 parallelly_1.37.1 DelayedArray_0.28.0 leiden_0.4.3.1 codetools_0.2-19 tidyselect_1.2.0 shape_1.4.6.1 farver_2.1.1 matrixStats_1.2.0 stats4_4.3.1 spatstat.explore_3.2-6 jsonlite_1.8.8 GetoptLong_1.0.5 ellipsis_0.3.2 progressr_0.14.0 ggridges_0.5.6 survival_3.5-8 iterators_1.0.14 foreach_1.5.2 tools_4.3.1 ica_1.0-3 Rcpp_1.0.12 glue_1.7.0 SparseArray_1.2.4 gridExtra_2.3 xfun_0.42 MatrixGenerics_1.14.0 GenomeInfoDb_1.38.7 withr_3.0.0 loo_2.7.0 fastmap_1.1.1 boot_1.3-30 fansi_1.0.6 digest_0.6.34 timechange_0.3.0 R6_2.5.1
[85] mime_0.12 gridGraphics_0.5-1 colorspace_2.1-0 scattermore_1.2 tensor_1.5 spatstat.data_3.0-4 utf8_1.2.4 generics_0.1.3 data.table_1.15.2 robustbase_0.99-2 S4Arrays_1.2.1 httr_1.4.7 htmlwidgets_1.6.4 uwot_0.1.16 pkgconfig_2.0.3 gtable_0.3.4 lmtest_0.9-40 SingleCellExperiment_1.24.0 XVector_0.42.0 htmltools_0.5.7 dotCall64_1.1-1 clue_0.3-65 Biobase_2.62.0 png_0.1-8 tzdb_0.4.0 rjson_0.2.21 curl_5.2.1 nlme_3.1-164 cachem_1.0.8 zoo_1.8-12 GlobalOptions_0.1.2 KernSmooth_2.23-22 parallel_4.3.1 miniUI_0.1.1.1 pillar_1.9.0 vctrs_0.6.5 RANN_2.6.1 promises_1.2.1 xtable_1.8-4 cluster_2.1.6 evaluate_0.23 magick_2.8.3
[127] cli_3.6.2 compiler_4.3.1 rlang_1.1.3 crayon_1.5.2 rstantools_2.4.0 future.apply_1.11.1 labeling_0.4.3 plyr_1.8.9 rstan_2.32.6 stringi_1.8.3 QuickJSR_1.1.3 deldir_2.0-4 munsell_0.5.0 lazyeval_0.2.2 spatstat.geom_3.2-9 V8_4.4.2 Matrix_1.6-5 RcppHNSW_0.6.0 hms_1.1.3 patchwork_1.2.0 bit64_4.0.5 future_1.33.1 shiny_1.8.0 SummarizedExperiment_1.32.0 ROCR_1.0-11 igraph_2.0.2 bslib_0.6.1 RcppParallel_5.1.7 bit_4.0.5 DEoptimR_1.1-3 References
Aitchison, J. 1982. “The Statistical Analysis of Compositional Data.” Journal of the Royal Statistical Society. Series B (Methodological) 44 (2): 139–77. https://www.jstor.org/stable/2345821.
Aitchison, John. 2005. “A Concise Guide to Compositional Data Analysis.” Research {Reports} or {Papers}. http://web.archive.org/web/20210423114827/http://ima.udg.edu/Activitats/CoDaWork05/.
Büttner, M., J. Ostner, C. L. Müller, F. J. Theis, and B. Schubert. 2021. “scCODA Is a Bayesian Model for Compositional Single-Cell Data Analysis.” Nature Communications 12 (1): 6876. https://doi.org/10.1038/s41467-021-27150-6.
Dann, Emma, Neil C. Henderson, Sarah A. Teichmann, Michael D. Morgan, and John C. Marioni. 2022. “Differential Abundance Testing on Single-Cell Data Using k-Nearest Neighbor Graphs.” Nature Biotechnology 40 (2): 245–53. https://doi.org/10.1038/s41587-021-01033-z.
Mangiola, Stefano, Alexandra J. Roth-Schulze, Marie Trussart, Enrique Zozaya-Valdés, Mengyao Ma, Zijie Gao, Alan F. Rubin, Terence P. Speed, Heejung Shim, and Anthony T. Papenfuss. 2023. “Sccomp: Robust Differential Composition and Variability Analysis for Single-Cell Data.” Proceedings of the National Academy of Sciences 120 (33): e2203828120. https://doi.org/10.1073/pnas.2203828120.
Morton, James T., Clarisse Marotz, Alex Washburne, Justin Silverman, Livia S. Zaramela, Anna Edlund, Karsten Zengler, and Rob Knight. 2019. “Establishing Microbial Composition Measurement Standards with Reference Frames.” Nature Communications 10 (1): 2719. https://doi.org/10.1038/s41467-019-10656-5.
Zheng, Hengqi Betty, Benjamin A. Doran, Kyle Kimler, Alison Yu, Victor Tkachev, Veronika Niederlova, Kayla Cribbin, et al. 2023. “Concerted Changes in the Pediatric Single-Cell Intestinal Ecosystem Before and After Anti-TNF Blockade.” eLife 12 (November). https://doi.org/10.7554/eLife.91792.1.